A machine learning model that predicts house prices based on features like size, location, and amenities. Helps buyers and sellers estimate property values accurately.
Last updated Jul 7, 2026
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README
🏠 California House Price Prediction App
An AI-powered interactive web application built with Streamlit that predicts median house prices in California based on housing features. The model is trained using the California Housing Dataset with Linear Regression and feature scaling.
🚀 Demo
🔗 Live App on Streamlit🚀 Video Demo
https://github.com/user-attachments/assets/1f5be119-6838-428f-b336-6883eb5aecdf📌 Features
- Predicts California house prices instantly.
- Simple sidebar input form for housing features.
- Powered by a Linear Regression model trained on real-world data.
- Uses a Scaler for accurate normalization of inputs.
- Clean and user-friendly Streamlit interface.
- Includes an About Me sidebar with portfolio links.
🔍 Usage
- Open the app in your browser.
- Enter the required housing features:
MedInc)
- House Age (HouseAge)
- Average Rooms (AveRooms)
- Average Bedrooms (AveBedrms)
- Population
- Average Occupancy (AveOccup)
- Latitude
- Click Predict House Price.
- The app will display the predicted median house value 💲.
📊 Dataset
- Dataset Name: California Housing Dataset
- Source: Scikit-learn Built-in Dataset
- Attributes:
MedInc – Median Income
- HouseAge – House Age
- AveRooms – Average Rooms
- AveBedrms – Average Bedrooms
- Population – Population in the area
- AveOccup – Average Occupancy
- Latitude – Latitude location
- Target: Median House Value
⚙️ Tech Stack
- Python 3.9+
- Streamlit (Web App UI)
- Scikit-learn (Linear Regression Model, Scaler)
- Pandas & NumPy (Data Processing)
- Pickle (Model Saving & Loading)
